After successful completion of the course, students are able to

• employ techniques from visualisation and visual analytics for the exploratory data analysis,
• employ techniques from visualisation and visual analytics for data presentation,
• employ strategies from human-computer-interaction (HCI) and perception to improve visualizations, and
• understand the differences between current software libraries and applications for data science.

There is also a possibility to analyze your own data.

• The lecture part will start with a theoretical introduction to visualisation, visual analytics and human-computer interaction (HCI).
• Afterwards practical applications of visualization in data science will be discussed.
• The lecture also contains a review on the differences between current software applications for data science.
• In the lab part students will get to know the differences between statistical and visual data analysis.
• Students will also directly compare different applications, and create a dashboard for the presentation of data analysis results.

Lecture with slides, programming examples, and live demos.

Further information can be found at the lecture webpage (https://www.cg.tuwien.ac.at/courses/VisDataScience/).

ECTS-Breakdown:
3 ECTS = 75 working hours, of which
  55 working hours (73%) are meant for the lab part, and
  20 working hours (27%) are meant for the lecture part

The contents taught in the lecture should be directly applied in practical exercise projects.

Registration can be done via TISS or TUWEL.

• Data Science basics
• Programming skills
• Preferrable knowledge about visualisation